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1. Final design and on-sky testing of the iLocater SX acquisition camera: broad-band single-mode fibre coupling

   https://doi.org/10.1093/mnras/staa3355  

   Crass, J; Bechter, A; Sands, B; King, D; Ketterer, R; Engstrom, M; Hamper, R; Kopon, D; Smous, J; Crepp, J R; et al (January 2021, Monthly Notices of the Royal Astronomical Society)

   null (Ed.)

   ABSTRACT Enabling efficient injection of light into single-mode fibres (SMFs) is a key requirement in realizing diffraction-limited astronomical spectroscopy on ground-based telescopes. SMF-fed spectrographs, facilitated by the use of adaptive optics (AO), offer distinct advantages over comparable seeing-limited designs, including higher spectral resolution within a compact and stable instrument volume, and a telescope independent spectrograph design. iLocater is an extremely precise radial velocity (EPRV) spectrograph being built for the Large Binocular Telescope (LBT). We have designed and built the front-end fibre injection system, or acquisition camera, for the SX (left) primary mirror of the LBT. The instrument was installed in 2019 and underwent on-sky commissioning and performance assessment. In this paper, we present the instrument requirements, acquisition camera design, as well as results from first-light measurements. Broad-band SMF coupling in excess of 35 per cent (absolute) in the near-infrared (0.97–1.31 $${\mu {\rm m}}$$) was achieved across a range of target magnitudes, spectral types, and observing conditions. Successful demonstration of on-sky performance represents both a major milestone in the development of iLocater and in making efficient ground-based SMF-fed astronomical instruments a reality. 

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2. Partial-Attribution Instance Segmentation for Astronomical Source Detection and Deblending

   Hausen, R.; Robertson, B. (December 2021, Fourth Workshop on Machine Learning and the Physical Sciences (NeurIPS 2021))

   Astronomical source deblending is the process of separating the contribution of individual stars or galaxies (sources) to an image comprised of multiple, possibly overlapping sources. Astronomical sources display a wide range of sizes and brightnesses and may show substantial overlap in images. Astronomical imaging data can further challenge off-the-shelf computer vision algorithms owing to its high dynamic range, low signal-to-noise ratio, and unconventional image format. These challenges make source deblending an open area of astronomical research, and in this work, we introduce a new approach called Partial-Attribution Instance Segmentation that enables source detection and deblending in a manner tractable for deep learning models. We provide a novel neural network implementation as a demonstration of the method. 

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3. 3D printing for teaching and exploration in astronomy for individuals with blindness/visual impairment: textured representations of imagery

   https://doi.org/10.1080/21548455.2024.2394906  

   Christian, Carol A; Nota, Antonella; Grice, Noreen; Madura, Thomas; Hurd, David (April 2025, International Journal of Science Education, Part B)

   Astronomy, a captivating field that draws upon science, mathematics, and engineering, has traditionally relied on visual representations to convey the wonders of the cosmos. While this approach effectively engages the sighted population, the use of imagery can exclude individuals with blindness or visual impairment (B/VI). Astronomical research is incorporated into press releases, media, outreach efforts, and educational systems aimed at enhancing public interest and often skill in science, but visual materials can hamper a population with B/VI. This paper explores the potential of 3D printing as an assistive technology providing an alternative to imagery. We produced textured 3D prints of astronomical research data from the Hubble Space Telescope (HST). Usability assessment of materials is an important phase of production before integration into structured programs, and we used a multi-phased approach in our prior research to create and test appropriate textures for 3D astronomical prints. This paper describes the last step of reviewing our 3D prints through informal usability sessions with diverse individuals. The assessment indicated our 3D prints provide reliable, informative representations of astronomical data appropriate for public use especially for public information, outreach programs, and science education for individuals with B/VI. 

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4. High Spectral Resolution Observations of Propynal (HCCCHO) toward TMC-1 from the GOTHAM Large Program on the GBT

   https://doi.org/10.3847/1538-4357/ad856e  

   Remijan, Anthony J; Fried, Zachary_T P; Cooke, Ilsa R; Wenzel, Gabi; Loomis, Ryan; Shingledecker, Christopher N; Lipnicky, Andrew; Xue, Ci; McCarthy, Michael C; McGuire, Brett A (November 2024, The Astrophysical Journal)

   Abstract We used new high spectral resolution observations of propynal (HCCCHO) toward TMC-1 and in the laboratory to update the spectral line catalog available for transitions of HCCCHO—specifically at frequencies lower than 30 GHz, which were previously discrepant in a publicly available catalog. The observed astronomical frequencies provided a high enough spectral resolution that, when combined with high-resolution (∼2 kHz) measurements taken in the laboratory, a new, consistent fit to both the laboratory and astronomical data was achieved. Now with a nearly exact (<1 kHz) frequency match to theJ= 2–1 and 3–2 transitions in the astronomical data, using a Markov Chain Monte Carlo analysis, a best fit to the total HCCCHO column density of ${7.28}_{-1.94}^{+4.08}\times {10}^{12}$cm⁻²was found with a surprisingly low excitation temperature of just over 3 K. This column density is around a factor of 5 times larger than reported in previous studies. Finally, this work highlights that care is needed when using publicly available spectral catalogs to characterize astronomical spectra. The availability of these catalogs is essential to the success of modern astronomical facilities and will only become more important as the next generation of facilities comes online. 

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5. Toward the Automated Detection of Light Echoes in Synoptic Surveys: Considerations on the Application of Deep Convolutional Neural Networks

   https://doi.org/10.3847/1538-3881/ac9409  

   Li, Xiaolong; Bianco, Federica B.; Dobler, Gregory; Partoush, Roee; Rest, Armin; Acero-Cuellar, Tatiana; Clarke, Riley; Fortino, Willow Fox; Khakpash, Somayeh; Lian, Ming (November 2022, The Astronomical Journal)

   Abstract Light echoes (LEs) are the reflections of astrophysical transients off of interstellar dust. They are fascinating astronomical phenomena that enable studies of the scattering dust as well as of the original transients. LEs, however, are rare and extremely difficult to detect as they appear as faint, diffuse, time-evolving features. The detection of LEs still largely relies on human inspection of images, a method unfeasible in the era of large synoptic surveys. The Vera C. Rubin Observatory Legacy Survey of Space and Time (LSST) will generate an unprecedented amount of astronomical imaging data at high spatial resolution, exquisite image quality, and over tens of thousands of square degrees of sky: an ideal survey for LEs. However, the Rubin data processing pipelines are optimized for the detection of point sources and will entirely miss LEs. Over the past several years, artificial intelligence (AI) object-detection frameworks have achieved and surpassed real-time, human-level performance. In this work, we leverage a data set from the Asteroid Terrestrial-impact Last Alert System telescope to test a popular AI object-detection framework, You Only Look Once, or YOLO, developed by the computer-vision community, to demonstrate the potential of AI for the detection of LEs in astronomical images. We find that an AI framework can reach human-level performance even with a size- and quality-limited data set. We explore and highlight challenges, including class imbalance and label incompleteness, and road map the work required to build an end-to-end pipeline for the automated detection and study of LEs in high-throughput astronomical surveys. 

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